Systems and methods for implementing an intelligent machine learning optimization platform for multiple tuning criteria

ABSTRACT

Systems and methods for tuning hyperparameters of a model includes: receiving a multi-criteria tuning work request for tuning hyperparameters of the model of the subscriber to the remote tuning service, wherein the multi-criteria tuning work request includes: a first objective function of the model to be optimized by the remote tuning service; a second objective function to be optimized by the remote tuning service, the second objective function being distinct from the first objective function; computing a joint tuning function based on a combination of the first objective function and the second objective function; executing a tuning operation of the hyperparameters for the model based on a tuning of the joint function; and identifying one or more proposed hyperparameter values based on one or more hyperparameter-based points along a convex Pareto optimal curve.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims the benefit of U.S. Provisional Application No.62/721,718, filed 23 Aug. 2018, which is incorporated herein in itsentirety by this reference.

TECHNICAL FIELD

The inventions relate generally to the computer optimization and machinelearning fields, and more specifically to a new and useful applicationprogram interface and intelligent hyperparameter optimization in thecomputer optimization and machine learning fields.

BACKGROUND

Modern machine learning capabilities are rapidly changing and improvinghow some of the most complex and data-intensive computing problems aresolved. A performance of a machine learning model is governed mainly inthe manner(s) in which the machine learning model is trained using datasamples as machine learning training input and based on thehyperparameters of the machine learning model set prior to the trainingof the model. As referenced in passing the hyperparameters of themachine learning models are parameters whose values are set prior to thecommencement of the machine learning process rather than derived by themachine learning model during training. Example include the number oftrees in a random forest or the number of hidden layers in a deep neuralnet. Adjusting the values of the hyperparameters of a machine learningmodel by any amount typically results in a large impact on a performanceof the machine learning model.

However, many existing machine learning models are not implemented withoptimal hyperparameters well-suited for achieving the best predictiveperformances. Rather, the many existing machine learning models areimplemented with default hyperparameters that have not been optimizedfor a specific computing problem for which the machine learning modelsare being used.

Additionally, any existing system that enables optimization ofhyperparameters of a machine learning model typically includes anextremely complex interface that may require significant codingcapabilities and comprehension of the underlying software and hardwarecomponents of the system. Thus, making it difficult to efficiently andeffectively enable optimizations and subsequent improvements of themachine learning models.

Thus, there is a need in the machine learning field to create animproved optimization platform to test and improve machine learningmodels (e.g., in-product machine learning models) and an associatedApplication Program Interface that enables developers to efficiently andeffectively interact with a robust system implementing the evaluationframework. The embodiments of the present application described hereinprovide technical solutions that address, at least, the need describedabove, as well as the technical deficiencies of the state of the artdescribed throughout the present application.

BRIEF DESCRIPTION OF THE FIGURES

FIG. 1 illustrates a schematic representation of a system in accordancewith one or more embodiments of the present application;

FIG. 2 illustrates a method for multi-criteria optimization inaccordance with one or more embodiments of the present application;

FIG. 2A illustrates a variant method for multi-criteria optimization inaccordance with one or more embodiments of the present application;

FIG. 3 illustrates a schematic representation of a system forimplementing an intelligent API in accordance with one or moreembodiments of the present application;

FIG. 4 illustrates schematic representation of a mixed system andprocess flow for implementing an intelligent optimization platform inaccordance with one or more embodiments of the present application;

FIG. 5 illustrates a schematic representation of multi-criteriaoptimization of a scalarization having a convex frontier in accordancewith one or more embodiments of the present application; and

FIG. 6 illustrates a schematic representation of multi-criteriaoptimization of a scalarization having a non-convex frontier inaccordance with one or more embodiments of the present application.

DESCRIPTION OF THE PREFERRED EMBODIMENTS

The following description of the preferred embodiments of the presentapplication are not intended to limit the inventions to these preferredembodiments, but rather to enable any person skilled in the art to makeand use these inventions.

Overview

As discussed above, existing machine learning models tend to lack inpredictive performance as well as speed in computation due to a lack ofoptimal hyperparameters used in the machine learning models duringtraining. The lack of optimized hyperparameters well-suited to anunderlying computing problem or the like adversely affect thecomputational capabilities of the machine learning model, in that, theresulting predictions or solutions of the model may not be accurate andthe speed of computation of the machine learning model may be slowbecause the un-optimized or poorly optimized hyperparameters of themodel may result in an increased workload (e.g., increased requiredcomputer processing, increased required data storage, etc.) to thecomputing system implementing the model and thereby resulting in manyefficiencies therein.

Additionally, even in the circumstance that an attempt is made tooptimize some of the hyperparameters of a machine learning model, theattempt to optimize the hyperparameters may fail due to many commonoptimization errors including: using inappropriate metrics andassumptions to test hyperparameters of an associated machine learningmodel or the like; overfitting a machine learning model during trainingthat often results in a poor fit of the model to out of sample data orunseen data; using too few hyperparameters and failing to optimize allhyperparameters of a machine learning model; unskilled or improperhand-tuning, which is a highly inefficient optimization strategy atwhich humans are poor at performing high dimensional, non-convexoptimization; grid searching over a space of possible hyperparameterswhich may grow the number of times a machine learning model must beevaluated due to an increase in dimensionality (i.e., increasedhyperparameters); random searching which uses no intelligence in theoptimization method and may result in unnecessarily high variance.

Accordingly, unintelligent optimization attempts of hyperparameters (orother model parameters) may result in high computational costs (e.g.,high computer processing resources expenditures, etc.).

The embodiments of the present application, however, provide anintelligent optimization platform that functions to optimizehyperparameters and/or parameters of any type of model withsignificantly fewer evaluation thereby saving computational resourceswhile greatly improving an overall performance of a model. Inembodiments of the present application, the intelligent optimizationplatform includes an ensemble of parameter optimization models, whichmay include a combination of several distinct machine learning modelsand Bayesian optimization algorithms that may work in selectivecombinations to expediently tune hyperparameters or various parametersof complex external systems, simulations, and models.

Further, the embodiments of the present application include an intuitiveand simplified Application Programming Interface (API) that enablesusers and/or developers to easily configure a work request, such as ahyperparameter optimization work request. A hyperparameter optimizationwork request as referred to herein generally relates to a request tooptimize one or more hyperparameters of a model. The hyperparameteroptimization work request may include an identification of thehyperparameters a user desires to optimize together with constraints orparameters required for experimenting or performing optimization trialsusing the system and/or methods described herein. The optimization workrequest may generally be generated using an API of the system 100, asdescribed below. In a preferred embodiment, the optimization workrequest functions to trigger an operation of the intelligentoptimization platform performing computations using the hyperparametersof the optimization work request. Additionally, in embodiments of thepresent application, using a limited number of simplified API calls, itis possible to integrate the sophisticated ensemble of Bayesianoptimization techniques of the intelligent optimization platform toaugment an existing machine learning pipeline.

Collaboratively, the intelligent optimization platform preferablyfunctions to improve the computational capabilities of a machinelearning model, such that the machine learning model performs at highlevels of accuracy and further, computes predictions, suggestions, andother outcomes faster (e.g., up to one hundred times faster or moreimprovement in machine learning models, etc.) than un-optimized orpoorly optimized machine learning models or other models. This, in turn,improves the functionality and operational speed and efficiency of theunderlying computing system executing the machine learning model orother model.

1. System for Implementing an Intelligent API

As shown in FIG. 1, a system 100 includes an intelligent applicationprogram interface (API) 105, an intelligent model optimization platform110, a plurality of queue working machines 120, a platform database 130,a shared work queue 135, and an ensemble of optimization models 140.

The system 100 preferably implements an intelligent model optimizationplatform 110 including an ensemble of Bayesian optimization processesand machine learning techniques that functions to automate anoptimization of features of a model, architecture of a model, andhyperparameters of a model using an ensemble of Bayesian optimizationtechniques, as described in U.S. patent application Ser. No. 15/977,168,which is incorporated herein in its entirety by this reference.

The system 100 functions to implement an intelligent Application ProgramInterface (API) 105, as described in U.S. Patent Application No.62/578,886, which is incorporated herein in its entirety by thisreference, for interacting and implementing complex optimization trialsvia the remote intelligent optimization platform 110. The API 105 may bespecifically designed to include a limited number of API endpoints thatreduce of complexity in creating an optimization work request,implementing optimization trials using the work request data, obtainingsuggestions and/or results of the optimization trials, and potentiallyimplementing an optimization feedback loop until a suitable optimizationof an objective function of the work request is achieved in a minimalamount of time. The optimization work request, as referred to herein,generally relates to an API request that includes one or morehyperparameters that a user is seeking to optimize and one or moreconstraints that the user desires for the optimization trials performedby the intelligent optimization platform 110.

In a preferred embodiment, the API 105 comprises a RepresentationalState Transfer (ReST) API that relies mainly on a stateless,client-server, cacheable communications protocol and in many cases, theRest API uses the HTIP protocol in connecting and interacting withsoftware applications over the web and cloud (distributed networksystems) services efficiently.

The API 105 may additionally be configured with logic that enables theAPI 105 to intelligently parse optimization work request data and/oraugment the optimization work request data with metadata prior topassing the optimization work request to the shared work queue 135 ofthe intelligent optimization platform 110. As shown in FIG. 4, a mixedsystem and process flow is provided that illustrates an exampleinteractions between the API 105 and one or more components of theintelligent optimization platform 110.

The intelligent optimization platform 110 includes the plurality ofqueue worker machines 120 (which may also be referred to herein asoptimization worker machines), the platform data 130, the shared workqueue 135 and the ensemble of optimization models 140. The intelligentoptimization platform 110 generally functions to interact with the APIserver implementing the API 105 to receive API requests for implementingnew optimization work requests and returning responses or suggestions tothe API 105. Using the plurality of intelligent queue worker machines120, the intelligent optimization platform 110 functions toasynchronously execute a plurality of optimization work requests inreal-time and in parallel. This asynchronous execution and parallelprocesses of the intelligent optimization system 110 provides speed incomputing efficiencies in the exploration and exploitation processes(generally, optimization) of features, hyperparameters, models andsystem architectures.

As shown by way of example in FIG. 3, the system enables a user toimplement and/or interact with the API 105 in multiple ways includingvia an API client application and/or via API web browser implementedover the web.

The intelligent optimization platform 110 may be implemented using acombination of computing servers. Preferably, the intelligentoptimization platform is implemented via a distributed networkedcomputing system, such as cloud computing systems, that allows the manyprocesses implemented by the intelligent optimization platform 110 to beimplemented in parallel and among disparate computers thereby, in someembodiments, mitigating the possibility of failure or bottlenecking inthe optimization pipeline of the intelligent optimization platform 110.Accordingly, the intelligent optimization platform 110 may beimplemented as a remote web service accessible by multiple clients overthe Internet, the Web, or any suitable communication network (e.g., aglobal area network, a wide area network, a local area network, etc.)that may function to place disparate computing resources in operableconnection and communication.

The plurality of intelligent queue worker machines 120 preferably relateto services operating on the intelligent optimization platform 110 thatexecutes code asynchronously with respect to other services or queueworking machines of the platform 110. In some embodiments, each of theplurality of intelligent queue worker machines 120 functions toselectively trigger one or more optimization requests to one or moreoptimization engines of the ensemble of optimization engines 140. And,once the work on the optimization request is completed by the selectedoptimization engine(s), the queue working machine returns the responsesor results to the platform database 130.

The plurality of intelligent queue worker machines 120 may bespecifically configured with logic that enables each of the machines 120to make dynamic and intelligent decisions in the selections of anensemble component of the plurality of ensemble of optimization models140. That is, each of the plurality of intelligent queue worker machinesmay function to selectively choose one or more optimization models ofthe ensemble 140 to execute one or more portions of an optimization workrequest.

The ensemble of optimization models 140 preferably includes a pluralityof disparate optimization models that operate to optimizehyperparameters, features, models, system architectures and the likeusing varying optimization algorithms. In a preferred embodiment, theensemble of optimization models 140 define a core optimization engine ofthe intelligent optimization platform 110. The features and theparameters of the core optimization engine comprising the ensemble ofoptimization models 140 may also be optimized continually by one or moreof the intelligent queue worker machines 120 (e.g., using Hyperopt,etc.).

The ensemble of optimization models 140 may include any number of modelsincluding, for example: a Low-Discrepancy sequence model, a MetricOptimization Engine (MOE) model (and variants thereof; e.g., MOE withone-hot encoding), a Tree-structured Parzen Estimators (TPE) model andvariants thereof, a Latin Hypercube model, a Swarm model, and the like.Each of these models of the example ensemble of optimization models mayfunction to encode categorical parameters differently from other membermodels of the ensemble and may include some interdependencies thatrequire combinations of the models to work together. Each of thesemodels may be individually selectable or selectable in combination by orusing the intelligent worker queue machines 120.

In a preferred embodiment, the plurality of intelligent queue workingmachines 120 may be implemented on a separate computing server than theAPI 105. In this way, long-running asynchronous processes do notadversely affect (e.g., slow down) a performance of an API computingserver and mainly, a capacity of the API computing server to service APIrequests.

Additionally, the plurality of intelligent queue worker machines 120include multiple, distinct intelligent queue worker machines 120 thatcoordinate optimization work request from the shared work queue 135received via the API 105 with the ensemble of optimization models 140.

A first example intelligent queue working machine may function toimplement Modelfit or Hyperopt that typically functions to tune one ormore of the hyperparameters of the optimization models of the ensembleconcurrently with the processing of the optimization work requestsreceived via the API 105. In one implementation, Modelfit or Hyperoptmay be used to tune hyperparameters of one of the optimization models ofthe ensemble 140. After receiving a set of observations based on thesuggestions for the set of hyperparameters, the first queue workermachine may implement Modelfit or Hyperopt to model fit thehyperparameters of the selected optimization models in order to generateimproved and new values for the set of hyperparameters via Nextpoints orthe like. A queue worker implementing Nextpoints may function to predictor suggest a new set of suggestions that include new parameter valuesfor a given model. In some embodiments, the first queue worker machinemay function to optimize the hyperparameters of the selectedoptimization models based on an evaluation a set of observationsreturned by a user.

A second example intelligent queue working machine may function toimplement Nextpoints that typically functions to generate or suggestnew, optimized values for the hyperparameters of the optimization workrequest. Accordingly, such intelligent queue working machine mayfunction to select one or more of the optimization models of theensemble 140, such as one or more machine learning models, forgenerating the new, optimized hyperparameter values.

A third example intelligent queue working machine may function toimplement an Importance algorithm that typically functions to judge ordetermine an importance of the hyperparameters submitted with theoptimization work request (e.g., hyperparameters of an external model).This example intelligent queue worker machine may additionally functionto analyze and determine an importance of features, hyperparameters, andarchitectures of the optimization models with respect to a givenoptimization work request; meaning the identified importancehyperparameters, features, or the like may have a significant impact onan account of a suggestion or generated hyperparameter values.Accordingly, the intelligent queue worker machine of such example mayfunction to recognize different hyperparameters and/or features of anoptimization model as being important and non-important based on theoptimization work request data (e.g., based on the hyperparameters to beoptimized). Thus, the intelligent queue worker machine may function toassign or attribute distinct importance values to the hyperparametersand/or features of the optimization models so that these hyperparametersand the like may be ranked and considered with greater weight in acorrelated process, such as re-tuning via Hyperopt or the like.

It shall be noted that the plurality of intelligent optimization workermachines 120 may not be limited to the above-noted examples, but ratheris an extensible group of intelligent machines that may be modified toinclude additional and/or different intelligent worker machines.

The platform database 130 functions to collect and stores any or allvalues generated by the system 100 including values generated whenexecuting an optimization work request by the intelligent optimizationplatform 110. Specifically, each of the plurality of intelligent queueworker machines may function to store within the platform database 130optimized hyperparameter values, optimized hyperparameter values of anoptimization work request, suggestions, surrogate models, partialinformation responses, and the like. The API 105 may be operablecommunication with the platform database 130 via a communication networkand may function to pull suggestions and/or response data via an APIcall or request.

The machine learning models, optimization models, and/or the ensemble ofmachine learning models may employ any suitable optimization algorithmsand/or machine learning including one or more of: supervised learning(e.g., using logistic regression, using back propagation neuralnetworks, using random forests, decision trees, etc.), unsupervisedlearning (e.g., using an Apriori algorithm, using K-means clustering),semi-supervised learning, reinforcement learning (e.g., using aQ-learning algorithm, using temporal difference learning), and any othersuitable learning style. Each module of the plurality can implement anyone or more of: a regression algorithm (e.g., ordinary least squares,logistic regression, stepwise regression, multivariate adaptiveregression splines, locally estimated scatterplot smoothing, etc.), aninstance-based method (e.g., k-nearest neighbor, learning vectorquantization, self-organizing map, etc.), a regularization method (e.g.,ridge regression, least absolute shrinkage and selection operator,elastic net, etc.), a decision tree learning method (e.g.,classification and regression tree, iterative dichotomiser 3, C4.5,chi-squared automatic interaction detection, decision stump, randomforest, multivariate adaptive regression splines, gradient boostingmachines, etc.), a Bayesian method (e.g., naïve Bayes, averagedone-dependence estimators, Bayesian belief network, etc.), a kernelmethod (e.g., a support vector machine, a radial basis function, alinear discriminate analysis, etc.), a clustering method (e.g., k-meansclustering, expectation maximization, etc.), an associated rule learningalgorithm (e.g., an Apriori algorithm, an Eclat algorithm, etc.), anartificial neural network model (e.g., a Perceptron method, aback-propagation method, a Hopfield network method, a self-organizingmap method, a learning vector quantization method, etc.), a deeplearning algorithm (e.g., a restricted Boltzmann machine, a deep beliefnetwork method, a convolution network method, a stacked auto-encodermethod, etc.), a dimensionality reduction method (e.g., principalcomponent analysis, partial lest squares regression, Sammon mapping,multidimensional scaling, projection pursuit, etc.), an ensemble method(e.g., boosting, boostrapped aggregation, AdaBoost, stackedgeneralization, gradient boosting machine method, random forest method,etc.), and any suitable form of machine learning algorithm. Eachprocessing portion of the system 100 can additionally or alternativelyleverage: a probabilistic module, heuristic module, deterministicmodule, or any other suitable module leveraging any other suitablecomputation method, machine learning method or combination thereof.However, any suitable machine learning approach can otherwise beincorporated in the system 100. Further, any suitable model (e.g.,machine learning, non-machine learning, etc.) can be used inimplementing the intelligent optimization platform 110 and/or othercomponents of the system 100.

The system 100 may additionally include a surrogate model generator(implemented via one or more of the optimization models 140) that may beimplemented by the intelligent optimization platform 110. Specifically,when an API request is received by the system 100 that requests a statusor suggestions of a partially completed (or incomplete) optimizationwork request, the intelligent optimization platform 110 may function toidentify candidate data points and other data (including suggestedhyperparameter values and optimized hyperparameters values) generated byeach of the plurality of intelligent queue worker machines 120 forresponding to the partially completed optimization work request andfurther, may trigger one or more of the optimization models of theensemble of optimization models to generate a surrogate (or proxy) modelthat can be used to test the uncertainty and/or the likelihood that acandidate data point would perform well in an external model. In oneexample, the system 100 may function to obtain hyperparameter values ofa most recent job of a first intelligent queue worker machineimplementing Hyperopt and cause one of the optimization models 140, suchas MOE, to generate the surrogate model using the hyperparameter valuesto test how well the candidate hyperparameter value data points mayperform.

The system 100 may also implement a ranking system 155 that functions torank multiple suggestions for a given optimization work request (oracross multiple optimization work requests for a given user) such thatthe suggestions having hyperparameter values most likely to perform thebest can be passed or pulled via the API 105. The ranking system 155 maybe implemented in any suitable manner including by the one or moreoptimization algorithms of the ensemble 140 that generated thesuggestions. For instance, if MOE is used to generate a plurality ofsuggestions for responding to an optimization work request, the system100 may function to use MOE to implement the ranking system 155.

It shall be noted that the sub-systems and components of the system 100may be connected or placed in operable communication using any suitablenetwork and any suitable manner. For instance, the components of thesystem 100 may be connected directly or indirectly over a network. Thenetwork may include any public (e.g., the Internet) or private network(e.g., intranet), a virtual private network, a wireless local areanetwork, a local area network, a wide area network, a wireless wide areanetwork, a global area network, a cellular network, any combination ofthe aforementioned and the like.

2. Method for Multiple Criteria Optimization Using an IntelligentOptimization Platform

2.1 Convex Frontier

As shown in FIG. 2, a method 200 for multi-criteria optimization byimplementing an intelligent optimization platform includes receiving anoptimization work request S210, configuring a scalarized function formultiple objective functions of a machine learning model S220,optimizing the scalarized function S230, identifying Pareto optimalsolutions for the scalarized function of the machine learning modelS240, and tuning and/or implementing the machine learning model S250.

The method 200 generally functions to enable a generation of Paretooptimal solution sets for optimizing competing performance objectivefunctions of a machine learning model. For instance, it may be anobjective or desire to improve, at a same time, one or more of anaccuracy, an error rate, validation loss, sparsity, an efficiency (speedof prediction or inference), and/or the like for a given machinelearning model. However, in many instances, if one performanceobjective, such as accuracy is improved, a second objective, such aspredictive efficiency or speed, may be diminished or degraded for themachine learning model. Thus, in such instances, there may besignificant tradeoffs in performance of disparate objectives when theyare optimized independently.

Accordingly, in one or more embodiments, the method 200 may function torecognize competing performance objectives of a machine learning modelin which an apparent inverse or divergent optimization relationship mayexist between two distinct and/or competing performance objectives ofthe machine learning model. The method 200 may function to overcome thisdivergent optimization scenario by implementing one or more techniquesthat enables a joint optimization of competing performance objectives ofa machine learning model that further enables a determination ofhyperparameter values that optimizes both of the competing objectivefunctions and consequently, the subject machine learning model alongboth of the competing performance objectives, as described in moredetail below.

Additionally, the method 200 may configure an intelligent optimizationplatform, in response to input values provided via an intelligentApplication Program Interface (API), such that the intelligentoptimization platform generates multiple Pareto optimal suggestions forhyperparameter values of competing objective functions of a scalarizedfunction. In the context of the present application, a Pareto optimalsolution for a given scalarized function and/or convex combination for amachine learning model generally relates to a solution that is notstrictly more optimal than another Pareto optimal solution. That is, thePareto optimal solutions identified along the Pareto-efficient frontierdefine a set of solutions where a first and second competing objectivemay be improved without sacrificing either objective.

S210, which includes receiving an optimization work request, functionsto receive an optimization work request comprising a multi-criteriaoptimization work request via an intelligent API. The multi-criteriaoptimization work request may be referred to herein as a multi-criteriatuning work request. Preferably, a multi-criteria optimization workrequest relates to an optimization request made to the intelligentoptimization platform that requires the optimization of two or morecriterion and/or two or more objective functions (or metrics) of asingle machine learning model rather than an optimization of oneobjective function of a single model. The intelligent API may beimplemented as a client application on a client device, such as a webbrowser, or any suitable interface accessible to a remote user system.Within the intelligent API, the remote user may be able to create ordefine the multi-criteria optimization work request (or experiment) byproviding one or more details of the objective functions or performanceobjectives that a user desires to optimize, hyperparameters and/or otherfeatures of machine learning model along with constraints (e.g.,optimization budgets, bounds, etc.) for performing the optimizationtrials by the intelligent optimization platform.

Accordingly, in some embodiments, a multi-criteria optimization workrequest may include an identification of at least a first metric (e.g.,predictive accuracy) of a machine learning model and a second metric(e.g., predictive efficiency/speed) of the machine learning model that auser desires to optimize. In such embodiments, each of the first metricand the second metric may be represented as a two-dimensional objectivefunction, e.g., ƒ1(x, y) and ƒ2(x, y), respectively. In one example, thevariables x and y of each of the objective functions ƒ1 and ƒ2 representprospective objective function values, which may both take values in arange in the continuous interval of 0 to 1 (i.e., [0, 1]), and “x” and“y” may represent possible hyperparameter values that may operate tooptimize ƒ1 and ƒ2, respectively. It shall be noted that, while in apreferred embodiment, the method 200 functions to implementtwo-dimensional objective functions, any value of multi-dimensionalobjective functions including, but not limited to, three-dimensionalobjective functions (e.g., x, y, z) may be implemented.

Optionally, S215, which includes preprocessing a multi-criteriaoptimization work request, functions to perform a preliminary evaluationof the two or more objective functions of the multi-criteriaoptimization work request to determine whether the two or more objectivefunctions for a given machine learning model compete or diverge inperformance when provided a random sample of objective function valuesfor each of the two or more objective functions. In some embodiments,the random values for each of the two or more objective functions may begenerated using a low-discrepancy optimization source that may functionto populate and/or compute random possible values for each of thefunctions.

Accordingly, S215 may function to validate whether the objectivefunctions identified within an optimization work request compete. Thatis, in some embodiments, S215 may function to validate the divergence orcompetition between distinct objective functions of a given model basedon similarities or differences in the outputs of the distinct objectivefunctions based on similar or same input. The input into the objectivefunctions may be selected from possible hyperparameter values for eachof the distinct objectives. In one or more embodiments, if S215determines that two or more of the objective functions compete, S215 mayfunction to trigger a signal for switching or selecting an optimizationmode of an intelligent optimization platform that performs anoptimization of the contending objective functions, as described herein.

S220, which includes computing a new joint objective function based onthe two or more competing objective functions, preferably functions togenerate a scalarized function based on the two or more competingobjective functions identified in the multi-criteria optimization workrequest. As referred to herein, a scalarization of the two or morecompeting objective functions preferably relates to a creation of asingle, joint objective function that combines the two or more competingobjective functions of a given model in a manner that enables a jointand/or simultaneous optimization of each of the two or more competingobjective functions using a single equation.

Referring again to the example above, the two example competingobjective functions, ƒ1(x, y) and ƒ2(x, y), maybe scalarized into asingle, joint objective function represented as g (x, y)=λ*ƒ1(x,y)+(1−λ)*ƒ2(x, y), where the new, joint objective function that isoptimized may be g (x, y). Effectively, the scalarization combines atleast two two-dimensional competing objective functions into a single,combined convex combination having two dimensions. The convexcombination preferably defines an interplay between the two or morecompeting objective functions. Specifically, the interplay between thetwo or more competing objective functions revealed by the convexcombination function may include a Pareto optimal solution along afrontier curve of points (i.e., possible solutions to the scalarizedfunction) having generally a convex shape. Accordingly, the scalarizedfunction g may sometimes be referred to herein as a convex combinationof ƒ1 and ƒ2. Still with respect to such example, a weighting or atuning factor represented by lambda (λ) may be constrained as follows:0<λ<1, in some embodiments. The lambda value λ may function to set ahyperplane and/or direction of optimization. The lambda values maygenerally enable a sweeping search for Pareto optimal values or defineregions of search. Accordingly, lambda may function as a weighting valuethat may enable greater or lesser optimization emphasis of either ƒ1 orƒ2 when its value is increased or decreased. For example, in thescalarized function, g (x, y)=λ*ƒ1(x, y)+(1−λ)*ƒ2(x, y), a larger lambdavalue may indicate a greater emphasis on the objective function ƒ1 and alesser emphasis on second objective function ƒ2. Conversely, a smallerlambda value may indicate a lesser emphasis on ƒ1 and a greater emphasison ƒ2. Accordingly, during an optimization and/or a tuning of thescalarized function as described in more detail in S230, the method 200may function to adjust values for λ together with providing values for“x” and “y”.

Once a scalarized function is defined for the two or more competingobjective functions of a machine learning model, S220 may function toprovide the scalarized function as optimization input into the one ormore optimization sources of the intelligent optimization platform. Thatis, in one or more embodiments, the intelligent optimization platformmay function to optimize the objective functions of the scalarization g(x, y). For instance, S220 may function to perform and/or execute tuningoperations that operate to identify potential hyperparameter values(i.e., “x” and “y”) for a given scalarized function, as described inU.S. Pat. Nos. 10,217,061 and 10,282,237, which are both incorporatedherein in their entireties by this reference.

S230, which includes optimizing the scalarized function, functions toconfigure the optimization settings of the intelligent optimizationplatform to enable optimization of the scalarized function foroptimizing a given machine learning model. In some embodiments, a basicconfiguration of the intelligent optimization platform includes settingsfor optimization of a single objective function that may typically be atwo-dimensional objective function. However, in the circumstances inwhich a multi-criteria optimization work request is received or detected(e.g., S215), S230 may function to switch or convert an optimizationmode of the intelligent optimization platform from a first optimizationmode (e.g., for optimizing a single objective function) to a secondoptimization mode for optimizing a generated scalarization functionbased on a multi-criteria optimization work request and/or based on areceived optimization mode selecting or switching signal (as provided byS215).

S230 may additionally or alternatively include S231, which includesimplementing a first optimization phase of the objective functions ofthe scalarized function of a machine learning model, functions toprovide one or more parameters of the scalarized function as input intoa first optimization source of the intelligent optimization platform.For instance, S231 may function to provide a minimum and maximum valuefor each of the two objective functions that define the scalarizedfunction. In this first optimization phase, S231 preferably functions toexplore a number of possible values for the objective functions of thescalarized function bounded between an upper and lower bound defined inthe multi-criteria optimization work request. That is, S231 may functionto (randomly) populate a field of potential values for each of theobjective functions of the scalarized function according to one or morepredetermined evaluation or testing constraints.

Accordingly, S231 may function to allocate a first portion (e.g., 20% ofoptimization budget) of an optimization budget (as further defined inthe multi-criteria optimization work request) to a low-discrepancyoptimization source or the like of the intelligent optimizationplatform. In turn, the low-discrepancy optimization source may functionto generate random values for the objective functions (e.g., values of xand y for g (x, y)) of the scalarized function that, in someembodiments, may be represented along a two-dimensional plane (ƒ1-ƒ2),as shown by example, in FIG. 5. A random distribution of values for theobjective functions of the scalarized function may, therefore, beidentified in S231.

S230 may additionally or alternatively include S233, which includesimplementing a second optimization phase of the objective functions ofthe scalarized function, functions to provide the scalarized function(as defined in S220) as input into a second optimization source of theintelligent optimization platform. In this second optimization phase,S233 may function to optimize values of the objective functions of thescalarized function by incrementally adjusting the scalarized functionby changing a lambda value. That is, in one example, S233 may functionto incrementally adjust values of lambda, λ, in a sweeping fashion (orany suitable manner) between the constraints of 0 and 1 to generatePareto optimal solutions sets along a Pareto-efficient frontier that maybe illustrated as a convex frontier. In such example, as a lambda valueof the scalarized function is incrementally adjusted or changed, S230may function to use the second optimization source to identify and/orgenerate optimal hyperparameter values for the scalarized function ateach given lambda setting and/or value. In this example, S230 mayfunction to test each of the random objective function values generatedby the low-discrepancy optimization source or the like within a space orregion defined by a selected lambda value.

In this second optimization phase, S233 may first function to allocate asecond portion (e.g., 60% of optimization budget) of an optimizationbudget to the second optimization source of the intelligentoptimization. Preferably, S233 allocates a larger portion of theoptimization budget to the second optimization phase relative to thefirst optimization phase (and a third optimization described furtherbelow). In some embodiments, a technical advantage of allocating alarger optimization (or testing) budget to the second optimization phaseenables a well-developed Pareto-efficient frontier that represents anumber of objective function values for the scalarized function thatjointly optimizes each of the two competing objective functions.Accordingly, depending on a desired performance of a machine learningmodel, a selection of an ordered pair of objective function values alongthe Pareto-efficient frontier for the scalarized function should yieldan optimal performance of each of the two competing objective functionsof the machine learning model.

As shown in FIG. 5, S233 may function to identify a frontier (e.g., aPareto optimal frontier) by dividing the lambda value of the scalarizedfunction into equal parts (e.g., six equal parts or the like), in one ormore embodiments, and sweeping the lambda values between each of theresulting sections of the Pareto optimal frontier. For example, if arange of lambda is between 0 and 1, [0,1], S233 may function topartition the values of lambda into four equal sections (e.g., 0-0.25,0.26-0.50, 0.51-0.75, and 0.76-1). Specifically, in some embodiments,once the range of lambda values of the scalarized function is dividedinto equal parts, S233 may function to incrementally adjust the lambdavalue of the scalarized function within each section or sub-range anduse the second optimization source to generate or identify objectivefunction values for the scalarized function within each distinctsubsection. Accordingly, after each incremental adjustment of the lambdavalue of the scalarized function within a divided lambda segment, theadjusted scalarized function may be provided as input into the secondoptimization source for generating or identifying objective functionvalues (i.e., new ordered pairs of x and y values for g (x, y)) or newpoints for the scalarized function. It shall be noted that while S233may preferably function to subdivide the total range of lambda valuesinto equal parts, S233 may alternatively divide the total range oflambda values in any suitable manner, including unequally, randomly, orother predetermined manner. For instance, in some embodiments, failureregions for a given scalarized function may be known. In such instance,less or no optimization resources may be allocated to such failureregions by diminishing a lambda-determined search region or byeliminating the region from an optimization search all together. Thatis, S233 may function to exclude one or more values for lambda in whichit may be known or in which there is a probability that correspondinghyperparameter values associated with a region of search set by the oneor more lambda values may fail to optimize the scalarized function.

In this second optimization phase, which may also be referred to hereinas the lambda sweeping phase, S233 functions to define a frontier ofPareto optimal values for the scalarization function by sweeping (e.g.,incrementally adjusting a lambda value from in a range of 0 to 1 or 1 to0 or the like). Preferably, each point on the frontier includes anordered pair of the objective functions achievable by the machinelearning model. It shall be noted that the Pareto-optimal solutions maynot necessarily be unique, as there can be multiple input combinationsof objective function values x and y that achieve a desired accuracy of0.8 and efficiency of 0.2, for example. In some embodiments, theresultant Pareto optimal frontier may typically define a convex arc witha plurality of Pareto optimal values defining the frontier and also,surrounding lower and upper sections of the frontier and with somePareto optimal values falling on or around the frontier arc.

S230 may additionally or alternatively include S235, which includesimplementing a third optimization phase of the objective functions ofthe scalarized function, functions to provide the scalarized function asinput into the second optimization source of the intelligentoptimization platform. In this third optimization phase, S235 mayfunction to optimize objective function values of the scalarizedfunction by using extreme values for lambda (e.g., 0 or near 0 valuesand 1 or near 1 values). That is, S235 may function to select and/oradjust the lambda values of the scalarized function to lambda valuesnear or at an upper bound of lambda (e.g., 1) and lambda values near orat a lower bound of lambda (e.g., 0). In this way, S235 may function topopulate the two edges of the Pareto optimal frontier with optimizedobjective function values for the scalarized function. Accordingly, thebest possible Pareto optimal solutions that best optimizes each of therespective two or more competing objective functions may be representedalong the frontier.

S240, which includes identifying Pareto optimal solutions for theobjective functions of the scalarized function, functions to construct agraphical representation based on a plurality of pairings of objectivefunction values (e.g., x, y values for g (x, y)) generated for thescalarized function. The graphical representation preferably includes adistribution of points defined by the plurality of pairings of objectivefunction values for the scalarized function. Additionally, oralternatively, the graphical representation of the generated objectivefunction values for the scalarized function preferably has twodimensions in which a first axis may represent values for a firstobjective function (e.g., ƒ1 (accuracy)) and a second axis may representvalues for a second objective function (e.g., ƒ2 (sparsity)) for a givenmachine learning model.

Within the graphical representation or the like, S240 may, additionallyor alternatively, function to identify dominating points within thedistribution of points or objective function values that wouldoutperform suboptimal points. S240 may function to identify dominatingpoints in any suitable manner including, but not limited to, identifyingclusters and/or areas along the frontier curve having a high density ofpoints. S240 may function to use the identified dominating points todefine a frontier curve along which the Pareto optimal solutions for thescalarized function may be found. In one or more embodiments, dependingon an input of a lambda (or desired relative importance of the competingobjective functions of the convex combination), S240 may function toidentify or select objective function values along the frontier curveand return the hyperparameter values that achieve the Pareto optimalobjective function values, as suggestions via the intelligent API.

S250, which includes tuning a machine learning model, functions to usethe generated or suggested identified hyperparameter values (derivedfrom a selected Pareto optimal objective function values) for thescalarized function to tune and/or otherwise, adjust the machinelearning model. In this regard, in one or more embodiments, theidentified hyperparameter values for the scalarized function mayfunction to dually optimize both of the competing objective functions ofthe given machine learning model. That is, Pareto optimal hyperparametervalues may function to improve a performance of a first objectivefunction while also improving and/or without sacrificing a performanceof a second, competing objective function of the machine learning model.

2.2 Non-Convex Frontier (Epsilon Constraint Method)

In some circumstances, it may be determined that a best fit curve or afrontier curve of the Pareto optimal values for a given scalarizationfunction may not be convex. In these circumstances, implementing anoptimization for the Pareto optimal values of a scalarized function byforcing a convex frontier curve onto the objective function values mayresult in a misidentification of a number of Pareto optimal values forthe scalarized function. Accordingly, in a variant of the method 200,S220 may function to configure the intelligent optimization platforminto a new optimization mode (e.g., a third mode, epsilon constraintoptimization mode) that enables the optimization of a conditionallyconstrained joint function in which a best fit curve of the Paretooptimal values may not be a convex curve.

Optionally, as a result of one or more measures or preliminaryevaluations of two or more objective functions of an optimization workrequest, S215 may function to generate a signal indicating that two ormore of the objective functions of a given machine learning modelcompete or diverge, when independently optimized (or potentially jointlyoptimized), and further, indicate an approximation of a type of Paretooptimal curve for a prospective scalarized function of a combination ofthe two or more competing objective functions or a conditionallyconstrained joint (combination) function of the two or more competingobjective functions. Accordingly, in the circumstances that S215 mayapproximate a non-convex frontier curve for the prospective jointfunction, S215 may trigger the epsilon constraint optimization mode ofthe intelligent optimization platform.

In a variant of 220, S220 may function to configure and/or compute a newconditionally constrained joint function for optimizing the two or morecompeting objective functions of multi-criteria optimization workrequest. Preferably, the new conditionally constrained joint functionenables the intelligent optimization platform to optimize a first of thetwo competing objectives of a single machine learning model subject to asecond of the two competing objective functions. S220 may additionallyor alternatively function to configure a conditionally constrained jointfunction in which the second of the two competing objectives is madesubject to the first of the two competing objective functions.

As an example, in some embodiments, S220 may function to formulate aconditionally constrained joint function of at least two of the two ormore competing objective functions of a multi-criteria optimization workrequest as follows:

-   -   Maximize ƒ1(x, y)    -   Subject to: ƒ2(x, y)>=ε1, ε2, ε3, . . . εN

In this example, the method 200 may function to optimize the objectivefunction ƒ1 subject to one or more epsilon constraints derived from theobjective function ƒ2.

In a variant of S231, S231 may function to populate or generate, via alow-discrepancy optimization source or the like, a random distributionof values for the objective functions of the conditionally constrainedjoint function. In some embodiments, the low discrepancy distribution ofobjective function values of the conditionally constrained jointfunction may be generated in S231 and reverted back to processes in S230in a further optimization mode decisioning or optimization modeselection step. Based on an approximate best fit curve of the objectivefunction values of the conditionally constrained joint function producedby the low discrepancy optimization source, S230 may function to switcha mode of the intelligent optimization source from a single criteriaoptimization or a basic multi-criteria optimization mode to amulti-criteria optimization/epsilon constraint mode.

In this variant as shown by way of example in FIG. 2A, S230 mayadditionally or alternatively include S237, which includes derivingepsilon constraint values based on segmenting values of the secondobjective function. In one embodiment, S237 may function to identifyepsilon constraint values by selecting segments or regions along an axisdefined by the second objective function (e.g., ƒ2). In oneimplementation, S230 may function to identify epsilon constraint valuesby dividing a range of the second objective function (e.g., ƒ2) intoplurality of different epsilon constraint levels (e.g., multiple epsilonvalues, ε1-ε6 . . . ). In performing the segmentation of the secondobjective function, a maximum and a minimum value of the secondobjective function may typically be required such that the epsilonconstrain levels exist between the maximum and minimum values of thesecond objective function. In some embodiments, S237 may consider theobserved maximum value and the minimum value of the second objectivefunction of the values generated by the low discrepancy optimizationsource as the maximum and the minimum values of the second objectivefunction. Additionally, or alternatively, in some embodiments, themaximum and minimum values of the second objective function may beprovided along with the multi-criteria optimization work request.

In a second implementation, S237 may function to identify epsilonconstraint values or levels based on the second objective function basedon predetermined and/or known failure regions for the second objectivefunction. In this regard, S237 may function to set one or more of theepsilon constrain values solely based on the identified region in whichthe second objective function cannot be optimized along with the firstobjective function. That is, these failure regions include regions inwhich one or more of the second objective function or the firstobjective function is degraded when the other of the two competingobjective functions is optimized.

Further with respect to this variant, S230 may additionally oralternatively include S239, which functions to progressively set epsilonconstraint values for optimizing the first objective function. In oneembodiment, S239 functions to use the epsilon constraint values,preferably derived from the second objective function, to progressivelyset exploration and/or optimization regions for the first objectivefunction, as shown by way of example in FIG. 6.

In use, S239 may function to set a first epsilon constraint value(e.g., 1) and provide the first epsilon constraint value and theconditionally constrained joint function as optimization input into thesecond optimization source of the intelligent optimization platform. Bysetting the first epsilon constraint value, S239 effectively defines orsets a failure region that causes the second optimization source tooptimize the objective functions of the conditionally constrained jointfunction in a region other than the failure region. Specifically, insome embodiments, the second optimization source may function tooptimize for values of the objective functions above the first epsilonconstraint value. Accordingly, in such embodiments, any points below thefirst epsilon constraint value may be dropped by the second optimizationsource as non-compliant or otherwise, non-optimal or failing values.

It shall be noted that while setting an epsilon constraint typicallyfunctions to set a failure region below the identified or the selectedepsilon constraint value, in one or more different embodiments, anepsilon constraint value may be selected or set that set a region aboveor laterally (to the left or right) of the selected epsilon constraintvalue to failure.

Accordingly, S239 may function to progressively set the epsilonconstraint values (e.g., ε1 and onward) until the non-convex frontiercurve and the plurality of optimized values for the objective functionsof the conditionally constrained joint function surrounding thenon-convex frontier curve are discovered. That is, S239 may function todiscover the dominant points of the non-convex curve associated with theoptimized conditionally constrained joint function.

S240, which includes identifying Pareto optimal solutions for thehyperparameters of the conditionally constrained joint function,functions to construct a graphical representation based on a pluralityof pairings of objective function values generated for the conditionallyconstrained joint function.

S250, which includes tuning a machine learning model, functions to usethe identified or suggested hyperparameter values (derived from aselected pairing of objective function values) for the conditionallyconstrained joint function to tune and/or otherwise, adjust the machinelearning model.

One or more instances of the method and/or processes described hereincan be performed asynchronously (e.g., sequentially), concurrently(e.g., in parallel), or in any other suitable order and/or using one ormore instances of the systems, elements, and/or entities describedherein.

The system and methods of the preferred embodiment and variationsthereof can be embodied and/or implemented at least in part as a machineconfigured to receive a computer-readable medium storingcomputer-readable instructions. The instructions are preferably executedby computer-executable components preferably integrated with the systemand one or more portions of the processors and/or the controllers. Thecomputer-readable medium can be stored on any suitable computer-readablemedia such as RAMs, ROMs, flash memory, EEPROMs, optical devices (CD orDVD), hard drives, floppy drives, or any suitable device. Thecomputer-executable component is preferably a general or applicationspecific processor, but any suitable dedicated hardware orhardware/firmware combination device can alternatively or additionallyexecute the instructions.

Although omitted for conciseness, the preferred embodiments includeevery combination and permutation of the implementations of the systemsand methods described herein.

As a person skilled in the art will recognize from the previous detaileddescription and from the figures and claims, modifications and changescan be made to the preferred embodiments of the invention withoutdeparting from the scope of this invention defined in the followingclaims.

What is claimed is:
 1. A system for tuning hyperparameters for improvingan effectiveness including one or more objective performance metrics ofa model, the system comprising: a remote tuning service for tuninghyperparameters of a model of a subscriber to the remote tuning service,wherein the remote tuning service is hosted on a distributed network ofcomputers that: receives a multi-criteria tuning work request for tuninghyperparameters of the model of the subscriber to the remote tuningservice, wherein the multi-criteria tuning work request includes atleast: (i) a first objective function of the model to be optimized bythe remote tuning service; (ii) a second objective function to beoptimized by the remote tuning service, the second objective functionbeing distinct from the first objective function; computes a jointtuning function based on a combination of the first objective functionand the second objective function of the model, wherein the joint tuningfunction comprises a scalarized function relating to a singular equationthat defines an interplay between the first objective function and thesecond objective function and enables a simultaneous optimization ofeach of the first objective function and the second objective function;wherein: (a) the first objective function is represented as ƒ1(x, y) andthe second objective function is represented as ƒ2(x, y); and (b) thescalarized function comprises:g(x,y)=λ*ƒ1(x,y)+(1−λ)*ƒ2(x,y), (c) x and y relate to potentialhyperparameter values selectable from a multi-dimensional coordinatesystem; (d) lambda, λ, relates to a tuning factor of the scalarizedfunction; executes a tuning operation of the hyperparameters for themodel based on a tuning of the joint function; and identifies one ormore proposed hyperparameter values based on one or morehyperparameter-based points along a convex Pareto optimal curve.
 2. Thesystem according to claim 1, wherein the first objective functionincludes a first multi-parameter function of the model having two ormore variables and is represented as ƒ1(V₁, V₂ . . . V_(n)), the secondobjective function includes a second multi-parameter function of themodel having two or more variables and is represented as ƒ2(V₁, V₂ . . .V_(n)), and the scalarized function comprises:g(V ₁ ,V ₂ . . . V _(n))=λ*ƒ1(V ₁ ,V ₂ . . . V _(n))+(1−λ)*ƒ2(V ₁ ,V ₂ .. . V _(n)), V₁, V₂ . . . V_(n) relate to potential hyperparametervalues selectable from a multi-dimensional coordinate system; andlambda, λ, relates to a tuning factor of the scalarized function.
 3. Thesystem according to claim 1, wherein the remote tuning service further:implements a first tuning phase of the joint function including: settingbounding parameters including bounding values for each of the firstobjective function and the second objective function; generating arandom distribution of possible hyperparameter values for the scalarizedfunction based on the bounding parameters.
 4. The system according toclaim 3, wherein the remote tuning service further: implements a secondtuning phase of the scalarized function including: incrementallyadjusting the lambda value of the scalarized function, wherein values oflambda are constrained between zero and one; intelligently selecting aplurality of hyperparameter values from the random distribution ofhyperparameter values based on a Bayesian optimization of the scalarizedfunction; and testing hyperparameter values selected from the randomdistribution of hyperparameter values.
 5. The system according to claim4, wherein the remote tuning service further: identifies the convexPareto optimal curve for the scalarized function based on identifying aplurality of distinct clusters of points based on a completion of thesecond tuning phase.
 6. The system according to claim 5, wherein theremote tuning service further: constructs a graphical representation ofthe Pareto optimal curve for the scalarized function including forcing aconvex curve line through the plurality of distinct clusters of points.7. The system according to claim 5, wherein the remote tuning servicefurther: selects a set of hyperparameter values along the convex Paretooptimal curve for the scalarized function; and returns, via anintelligent application programming interface, the set of hyperparametervalues to the subscriber.
 8. The system according to claim 7, whereinthe model of the subscriber comprises a machine learning model, themachine learning model is implemented with the set of hyperparametervalues that jointly improves performances of the first objectivefunction and the second objective function of the machine learningmodel.
 9. The system according to claim 4, wherein the Pareto optimalcurve for the scalarized function includes distinct pairs (x, y) ofhyperparameter values for the scalarized function where the firstobjective function and the second objective function are improvedwithout sacrificing a performance of either of the first objective andthe second objective.
 10. The system according to claim 1, wherein eachvalue for lambda sets a hyperplane defining a distinct region of searchfor hyperparameter values of the model.
 11. The system according toclaim 1, wherein the remote tuning service: sets one or more failureregions based on restricting searches of hyperparameter values based onone or more restricting one or more possible values of lambda.
 12. Thesystem according to claim 1, further comprising: an applicationprogramming interface that is in operable communication with the remotetuning service and that: configures the multi-criteria tuning request,wherein configuring the multi-criteria tuning request includes: definingthe first objective function of the model, and defining the secondobjective function of the model.
 13. A method for tuning hyperparametersfor improving an effectiveness including one or more objectiveperformance metrics of a model, the method comprising: receiving aremote tuning service for tuning hyperparameters of a model of asubscriber to the remote tuning service, wherein the remote tuningservice is hosted on a distributed network of computers; receiving amulti-criteria tuning work request for tuning hyperparameters of themodel of the subscriber to the remote tuning service, wherein themulti-criteria tuning work request includes at least: (i) a firstobjective function of the model to be optimized by the remote tuningservice; (ii) a second objective function to be optimized by the remotetuning service, the second objective function being distinct from thefirst objective function; computing a joint tuning function based on acombination of the first objective function and the second objectivefunction of the model, wherein the joint tuning function comprises ascalarized function relating to a singular equation that defines aninterplay between the first objective function and the second objectivefunction and enables a simultaneous optimization of each of the firstobjective function and the second objective function; wherein: (a) thefirst objective function is represented as ƒ1(x, y) and the secondobjective function is represented as ƒ2(x, y); (b) the scalarizedfunction comprises:g(x,y)=λ*ƒ1(x,y)+(1−λ)*ƒ2(x,y), (c) x and y relate to potentialhyperparameter values selectable from a multi-dimensional coordinatesystem; and (d) lambda, λ, relates to a tuning factor of the scalarizedfunction; executing a tuning operation of the hyperparameters for themodel based on a tuning of the scalarized function; and identifying oneor more proposed hyperparameter values based on one or morehyperparameter-based points along a convex Pareto optimal curve.
 14. Themethod according to claim 13, further comprising: implementing a firsttuning phase of the joint function including: setting boundingparameters including bounding values for each of the first objectivefunction and the second objective function; generating a randomdistribution of possible hyperparameter values for the scalarizedfunction based on the bounding parameters.
 15. The method according toclaim 14, further comprising: implementing a second tuning phase of thescalarized function including: incrementally adjusting the lambda valueof the scalarized function, wherein values of lambda are constrainedbetween zero and one; and test hyperparameter values selected from therandom distribution of hyperparameter values based on each incrementaladjustment of the lambda value.
 16. A system for tuning hyperparametersfor improving an effectiveness including one or more objectiveperformance metrics of a model, the system comprising: a remote tuningservice for tuning hyperparameters of a model of a subscriber to theremote tuning service, wherein the remote tuning service is hosted on adistributed network of computers that: receives a multi-criteria tuningwork request for tuning hyperparameters of the model of the subscriberto the remote tuning service, wherein the multi-criteria tuning workrequest includes at least: (i) a first objective function of the modelto be optimized by the remote tuning service; (ii) a second objectivefunction to be optimized by the remote tuning service, the secondobjective function being distinct from the first objective function;computes a joint tuning function based on a combination of the firstobjective function and the second objective function of the model,wherein the joint tuning function comprises a scalarized functionrelating to a singular equation that defines an interplay between thefirst objective function and the second objective function and enables asimultaneous optimization of each of the first objective function andthe second objective function; wherein: (a) the first objective functionincludes a first multi-parameter function of the model having two ormore variables and is represented as ƒ1(V₁, V₂ . . . V_(n)), (b) thesecond objective function includes a second multi-parameter function ofthe model having two or more variables and is represented as ƒ2(V₁, V₂ .. . V_(n)), and (c) the scalarized function comprises:g(V ₁ ,V ₂ . . . V _(n))=λ*ƒ1(V ₁ ,V ₂ . . . V _(n))+(1−λ)*ƒ2(V ₁ ,V ₂ .. . V _(n)), V₁, V₂ . . . V_(n) relate to potential hyperparametervalues selectable from a multi-dimensional coordinate system; (d)lambda, λ, relates to a tuning factor of the scalarized functionexecutes a tuning operation of the hyperparameters for the model basedon a tuning of the joint function; and identifies one or more proposedhyperparameter values based on one or more hyperparameter-based pointsalong a convex Pareto optimal curve.